{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torchvision\n",
    "import torch\n",
    "from torch import nn\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入模型\n",
    "class CNN(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(CNN,self).__init__()\n",
    "        self.conv1 = nn.Conv2d(1,32,kernel_size=3,stride=1,padding=1)\n",
    "        self.pool = nn.MaxPool2d(2,2)\n",
    "        self.conv2 = nn.Conv2d(32,64,kernel_size=3,stride=1,padding=1)\n",
    "        self.fc1 = nn.Linear(64*7*7,1024)#两个池化，所以是7*7而不是14*14\n",
    "        self.fc2 = nn.Linear(1024,512)\n",
    "        self.fc3 = nn.Linear(512,10)\n",
    "#         self.dp = nn.Dropout(p=0.5)\n",
    "    def forward(self,x):\n",
    "        x = self.pool(F.relu(self.conv1(x)))\n",
    "        x = self.pool(F.relu(self.conv2(x)))\n",
    "\n",
    "        x = x.view(-1, 64 * 7* 7)#将数据平整为一维的\n",
    "        x = F.relu(self.fc1(x))\n",
    "#         x = self.fc3(x)\n",
    "#         self.dp(x)\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = self.fc3(x)\n",
    "#         x = F.log_softmax(x,dim=1) NLLLoss()才需要，交叉熵不需要\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 检测设备\n",
    "dev = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "# 预处理变换\n",
    "transform = torchvision.transforms.Compose([\n",
    "    torchvision.transforms.ToTensor(),\n",
    "    torchvision.transforms.Normalize(mean=[0.5],std=[0.5])\n",
    "])\n",
    "\n",
    "# 从本地读取图片并进行预处理\n",
    "res_label = []\n",
    "res_img = []\n",
    "for i in range(1, 30):\n",
    "    img_path = f'./pics/{i}.jpg'\n",
    "    img = transform(Image.open(img_path))\n",
    "    res_img.append(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载模型\n",
    "model = CNN()\n",
    "# 加载State模型\n",
    "model.load_state_dict(torch.load(\"./mnist_net.pth\"))\n",
    "# 加载数据到GPU\n",
    "model.to('cuda')\n",
    "# 加载Full模型\n",
    "# model = torch.load(\"./mnist_net_full.pth\")\n",
    "\n",
    "# 开启评估模式\n",
    "model.eval()\n",
    "# 禁用梯度计算\n",
    "with torch.no_grad():\n",
    "    # 遍历素材并进行预测\n",
    "    for img in res_img:\n",
    "        i = torch.reshape(img, (1, 28, 28)).to(dev)\n",
    "        _, preds = torch.max(model(i), dim=1)\n",
    "        res_label.append(preds.cpu().numpy()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘图展示\n",
    "hight, width = 5, 6\n",
    "fig, axes = plt.subplots(hight, width, figsize=(10, 10))\n",
    "for i in range(hight):\n",
    "    for j in range(width):\n",
    "        idx = 1 * i + j\n",
    "        axes[i][j].imshow(res_img[idx].squeeze(), cmap=\"gray\")\n",
    "        axes[i][j].set_title(res_label[idx])\n",
    "        axes[i][j].axis('off')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
